Antelope Project Analysis

Behavioral Data

Demographics

First, let’s load the data and transform it into a Wide format table

behav <- read_tsv("data/behav/behav_data.txt",
                  col_types=cols())

behav$material[behav$material=="Swahili"] <- "Vocabulary"
behav$material[behav$material=="US Maps"] <- "Maps"

behav <- behav %>% rename(Gender = gender, Age = age)

wbehav <- behav %>% pivot_wider(values_from = alpha,
                                id_cols = c(Subject, Gender, Age),
                                names_from = material)

At this point, we can look at the participant demographocs

ggplot(data=wbehav, aes(x=Age, col=Gender)) +
  geom_histogram(aes(col="white", fill=Gender),
                 colour="white", alpha=0.5, 
                 position="identity", binwidth = 1) +
  scale_color_brewer(palette = "Set1") +
  scale_fill_brewer(palette = "Set1") +
  ggtitle("Age Distribution by Gender") + 
  theme_pander()

## Warning: Removed 2 rows containing non-finite values (stat_bin).

Rate of Forgetting (Alpha parameter) for the two tasks

We can now examine the distribution and correlation of the rate of forgetting for the verbal (Vocabulary) and Maps:

mu <- behav %>% 
  group_by(material) %>% 
  summarize(alpha=mean(alpha))

p1 <- ggplot(behav, aes(x=alpha, fill=material)) +
  geom_histogram(col="white", binwidth = 0.025, 
                 alpha=0.5, position="identity") +
  scale_color_brewer(palette = "Dark2") +
  scale_fill_brewer(palette = "Dark2") +
  geom_vline(data=mu, aes(xintercept=alpha, color=material),
             linetype="dashed") +
  xlab(expression(paste("Estimated value of ", alpha))) +
  theme_pander() +
  ylab("Count") +
  ggtitle("(A) Distribution By Materials") +
  theme(legend.position = c(0.2, 0.8)) +
  theme(plot.title = element_text(hjust = 0.5))

p2 <- ggplot(wbehav, aes(x=Vocabulary, y=Maps)) +
  geom_point(size=4, alpha=0.5, col="black") + #col=K[7]) +
  geom_smooth(method = "lm", formula = y ~ x, 
              col="red", fill="red", fullrange = T, lwd=2) +
  theme_pander() +
  scale_x_continuous() + 
  scale_y_continuous() + 
  ggtitle("(B) Correlation Across Materials") +
  xlab(expression(paste(alpha, " Vocabulary"))) +
  ylab(expression(paste(alpha, " Maps"))) +
  geom_rug(col="black", lwd=1, alpha=.5) +
  annotate("segment", x=0.1, y=0.1, xend=0.5, 
           yend=0.5, col="grey", lwd=1, lty=2) +
  theme(plot.title = element_text(hjust = 0.5))
  
grid.arrange(p1, p2, ncol=2)

p3 <- ggplot(wbehav, aes(x=Vocabulary, y=Maps)) +
  geom_point(size=4, alpha=0.5, col="black") + #col=K[7]) +
  geom_smooth(method = "lm", formula = y ~ x, col="red", 
              fill="red", fullrange = T, lwd=2) +
  theme_pander() +
  scale_x_continuous() + 
  scale_y_continuous() + 
  coord_fixed() +
  ggtitle("Correlation of Alpha Parameters\nAcross Materials") +
  xlab(expression(paste(alpha, " Vocabulary"))) +
  ylab(expression(paste(alpha, " Maps"))) +
  annotate("segment", x=0.1, y=0.1, xend=0.5, 
           yend=0.5, col="grey", lwd=1, lty=2) +
  annotate("text", label = paste("r =", round(cor(wbehav$Vocabulary,
                                                  wbehav$Maps),
                                              2)), 
                                 x=0.2, y=0.45, col="red") +
  
  theme(plot.title = element_text(hjust = 0.5))

ggMarginal(p3, col="white", fill="black", alpha=0.5,
           type="hist", bins=13)

ggsave("images/figure1.png", ggMarginal(p3, col="white", fill="black", alpha=0.5,
           type="hist", bins=13),  width=5, height = 5, dpi = 300)

And here is a summary of the data:

summary(wbehav) %>%
  xtable() %>%
  kable(digits = 3) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

	Subject	Gender	Age	Vocabulary	Maps
X	Length:50	Length:50	Min. :18.00	Min. :0.1219	Min. :0.2120
X.1	Class :character	Class :character	1st Qu.:18.00	1st Qu.:0.2647	1st Qu.:0.3039
X.2	Mode :character	Mode :character	Median :19.00	Median :0.2917	Median :0.3447
X.3	NA	NA	Mean :18.92	Mean :0.2825	Mean :0.3355
X.4	NA	NA	3rd Qu.:19.00	3rd Qu.:0.3203	3rd Qu.:0.3680
X.5	NA	NA	Max. :22.00	Max. :0.3852	Max. :0.4701
X.6	NA	NA	NA’s :2	NA	NA

Eyes-Closed EEG Data

Now we look at the neural correlates of the rates of forgetting in EEG data. We begin with eyes closed EEG data, which is the most common type of resting-state EEG recording.

(Optional) Preprocess the Data

Set the process_raw variable to TRUE to reprocess the raw EEG data. That might take a very long time.

process_raw = F
if (process_raw) {
  setwd("./data/eyes_closed/")
  for (sub in dir(grep("A[1-9]", dir()))) {
    setwd(sub)
    analyze.logfile(sub, "closed")
    setwd("..")
  }
  setwd("../..")
}

Load the Eyes-Closed Spectra and Summary Files

The preprocessing step produces a number of text files. Here, we are interested in the summary file, which contains many useful statistics, including the Individual Alpha Frequency (i.e., the Alpha Peak) for each channel, the spectra file, which contains the estimated log power for each channel in increments of 0.5 Hz.

ec_spectra <- NULL
for (sub in dir("data/eyes_closed/")[grep("A[1-9]",
                                        dir("data/eyes_closed/"))]) {
  table <- read_tsv(paste("data/eyes_closed/", sub, "/", 
                          sub, "_closed_spectra.txt", sep=""),
                    col_types = cols())
  if (is.null(ec_spectra)) {
    ec_spectra <- table
  } else {
    ec_spectra <- ec_spectra %>% bind_rows(table)
  }
}

ec_summary <- NULL
for (sub in dir("data/eyes_closed/")[grep("A[1-9]", 
                                          dir("data/eyes_closed/"))]) {
  table <- read_tsv(paste("data/eyes_closed/", sub, "/", 
                          sub, "_closed_summary.txt", sep=""),
                    col_types = cols())
  if (is.null(ec_summary)) {
    ec_summary <- table
  } else {
    ec_summary <- ec_summary %>% bind_rows(table)
  }
}

Individual Alpha frquency

The IAF is perhaps the most important subject level characteristic of the resting state spectrum. For this reason, we want to get some basic statistics. First, we calculate the subject-level IAF by computing the mode IAF across all channels for a given participant.

iaf_cols <- paste(c("AF3", "AF4", "F3", "F4", 
                    "F7", "F8", "FC5", "FC6", 
                    "T7", "T8", "P7", "P8", 
                    "O1", "O2"), 
                  "IAF", 
                  sep="_")

ec_iafs <- ec_summary %>%
  select(c("Subject", iaf_cols)) %>%
  filter(Subject %in% wbehav$Subject) %>%
  pivot_longer(cols = iaf_cols, names_sep="_", 
               names_to = c("Channel", "Discard"), 
               values_to = "IAF_EC") %>%
  select(Subject, Channel, IAF_EC) %>%
  group_by(Subject) %>%
  summarize(IAF_EC = Mode(IAF_EC))

## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(iaf_cols)` instead of `iaf_cols` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.

The mean IAF thus calculated is 10.3, which is exactly in the middle of our alpha frequency band.

ggplot(data=ec_iafs, aes(x = IAF_EC)) +
  geom_histogram(aes(col="white"), fill="black", 
                 colour="white", alpha=0.5, 
                 position="identity", binwidth = 0.5) +
  ggtitle("IAF Distribution, Eyes Closed") + 
  xlab("IAF (Eyes Closed)") +
  theme_pander()

Average Spectrum

First, let’s examine the spectrograms for each channel to make sure it looks normal and the IAF (individual alpha frequency) is reasonable, i.e., the same for each channel and roughly in the middle of the band definition

l_ec_spectra <- pivot_longer(ec_spectra, cols=names(ec_spectra)[3:130], 
                         names_to="Freq")

l_ec_spectra <- l_ec_spectra %>% 
  rename(Power = value) %>%
  add_column(Recording = "Eyes Closed")

l_ec_spectra$Frequency <- as.numeric(substr(l_ec_spectra$Freq, 
                                            0, str_length(l_ec_spectra$Freq) -2))
l_ec_spectra <- l_ec_spectra %>% 
  add_column(Band="Delta", BandMin=0, BandMax=4) 
l_ec_spectra$Band[l_ec_spectra$Frequency <= 40] <- "Gamma"
l_ec_spectra$Band[l_ec_spectra$Frequency < 30] <- "High Beta"
l_ec_spectra$Band[l_ec_spectra$Frequency < 18] <- "Upper Beta"
l_ec_spectra$Band[l_ec_spectra$Frequency < 15] <- "Low Beta"
l_ec_spectra$Band[l_ec_spectra$Frequency < 13] <- "Alpha"
l_ec_spectra$Band[l_ec_spectra$Frequency < 8] <- "Theta"
l_ec_spectra$Band[l_ec_spectra$Frequency < 4] <- "Delta"

l_ec_spectra$BandMin[l_ec_spectra$Frequency <= 40] <- 30
l_ec_spectra$BandMin[l_ec_spectra$Frequency < 30] <- 18
l_ec_spectra$BandMin[l_ec_spectra$Frequency < 18] <- 15
l_ec_spectra$BandMin[l_ec_spectra$Frequency < 15] <- 13
l_ec_spectra$BandMin[l_ec_spectra$Frequency < 13] <- 8
l_ec_spectra$BandMin[l_ec_spectra$Frequency < 8] <- 4
l_ec_spectra$BandMin[l_ec_spectra$Frequency < 4] <- 0

l_ec_spectra$BandMax[l_ec_spectra$Frequency <= 40] <- 40
l_ec_spectra$BandMax[l_ec_spectra$Frequency < 30] <- 30
l_ec_spectra$BandMax[l_ec_spectra$Frequency < 18] <- 18
l_ec_spectra$BandMax[l_ec_spectra$Frequency < 15] <- 15
l_ec_spectra$BandMax[l_ec_spectra$Frequency < 13] <- 13
l_ec_spectra$BandMax[l_ec_spectra$Frequency < 8] <- 8
l_ec_spectra$BandMax[l_ec_spectra$Frequency < 4] <- 4

l_ec_spectra$Band <- factor(l_ec_spectra$Band, 
                        levels = c("Delta", "Theta", "Alpha", 
                                   "Low Beta", "Upper Beta",
                                   "High Beta", "Gamma"))

Now, we remove the three participants for which we have poor quality data

l_ec_spectra <- l_ec_spectra %>%
  filter(Subject %in% behav$Subject)

We can visualize the power spectra to ensure that our data looks normal

gd <- l_ec_spectra %>% 
        group_by(Band) %>% 
        summarise(
          Min = mean(BandMin),
          Max = mean(BandMax),
          Power =mean(Power),
          Frequency = mean(BandMin)
          #Channel=
        )

ggplot(data=l_ec_spectra, aes(x=Frequency, y=Power, Channel)) +
  geom_rect(data = gd, aes(xmin = Min, xmax = Max, fill = Band), 
            ymin=0, ymax=Inf, colour=NA, alpha=0.5) +
  stat_summary(fun.data=mean_sdl, 
               geom = "ribbon", colour = "white", alpha = 0.4) +
  stat_summary(fun = mean, geom = "line", lwd = 1) +
  facet_wrap(~ Channel, ncol=4) +
  scale_alpha_manual(values = seq(0.1, 0.9, 0.1)) +
  ggtitle("Eyes-Closed Power Spectrum Across Channels") +
  ylab("Log Power") + 
  xlab("Frequency (Hz)") + 
  theme_pander() +
  coord_cartesian(xlim=c(1,40), ylim=c(5, 17)) + #ylim=c(5,17)) +
  scale_fill_jco() +
  theme(plot.title = element_text(hjust = 0.5))

Now, let’s create a mean spectrogram with annotations that mark the different frequency bands. It serves as a visual reference that our data is not grossly out of whack.

#K = brewer.pal(7, "Set3")
jco <- pal_jco()
K = jco(7)

al_ec_spectra <- aggregate(l_ec_spectra[c("Power")],
                       list(Subject = l_ec_spectra$Subject, 
                            Frequency = l_ec_spectra$Frequency, 
                            Band = l_ec_spectra$Band), 
                       mean)

ggplot(data=al_ec_spectra, aes(x=Frequency, y=Power, col=Power)) +
  annotate("rect", xmin = 0, xmax = 4, ymin = 0, ymax = Inf, 
           alpha = 0.5, fill=K[1]) +
  annotate("rect", xmin = 4, xmax = 8, ymin = 0, ymax = Inf, 
           alpha = 0.5, fill=K[2]) +
  annotate("rect", xmin = 8, xmax = 13, ymin = 0, ymax = Inf, 
           alpha = 0.5, fill=K[3]) +      
  annotate("rect", xmin = 13, xmax = 15, ymin = 0, ymax = Inf, 
           alpha = 0.5, fill=K[4]) +
  annotate("rect", xmin = 15, xmax = 18, ymin = 0, ymax = Inf, 
           alpha = 0.5, fill = K[5]) +
  annotate("rect", xmin = 18, xmax = 30, ymin = 0, ymax = Inf, 
           alpha = 0.5, fill=K[6]) +
  annotate("rect", xmin = 30, xmax = 40, ymin = 0, ymax = Inf,
           alpha = 0.5, fill=K[7]) +
  stat_summary(fun.data = mean_sdl, geom = "ribbon", 
               alpha = 0.5, fill = "grey50", col = "white") +
  stat_summary(fun=mean, geom="line", lwd=2) +
  coord_cartesian(xlim=c(0,40), ylim=c(5,18)) +
  xlab("Frequency (Hz)") +
  theme_pander() +
  annotate("text", x=2, y=5, label="Delta", angle=90, hjust=0) +
  annotate("text", x=6, y=5, label="Theta", angle=90, hjust=0) +
  annotate("text", x=10.5, y=5, label="Alpha", angle=90, hjust=0) +
  annotate("text", x=14, y=5, label="Low Beta", angle=90, hjust=0) +
  annotate("text", x=16.5, y=5, label="Upper Beta", angle=90, hjust=0) +
  annotate("text", x=24, y=5, label="High Beta", angle=90, hjust=0) +
  annotate("text", x=35, y=5, label="Gamma", angle=90, hjust=0) 

Correlations with Rate of Forgetting

Having ensured that our EEG recordings look normal, reflect known neurophysiological characteristics, and that our frequency bands boundaries are reasonable, we can proceed with correlating the rates of forgetting with resting state QEEG characteristics.

First, we calculate the mean power for every band and channel, remove participants who were not included in the behavioral tests, and correct for multiple comparisons by frequency band:

r2p <- function(r, n) {
  df = n - 2
  t = (r * sqrt(df)) / sqrt(1 - r**2)
  2 * pt(-abs(t), df = df)
}

wbehav <- wbehav %>%
  mutate(GlobalAlpha=(Vocabulary + Maps)/2)

Adata_ec <- l_ec_spectra %>% 
  group_by(Subject, Channel, Band, Recording) %>% 
  summarize(Power=mean(Power))

## `summarise()` has grouped output by 'Subject', 'Channel', 'Band'. You can override using the `.groups` argument.

Fdata_ec <- inner_join(Adata_ec, behav, by="Subject")

Rdata_ec <- Fdata_ec %>%
  group_by(material, Channel, Band, Recording) %>%
  summarise(r = cor(Power, alpha),
            rob_r =robcor(Power, alpha, method="ssd"),
            p = cor.test(Power, alpha)$p.value)

## `summarise()` has grouped output by 'material', 'Channel', 'Band'. You can override using the `.groups` argument.

Rdata_ec <- Rdata_ec %>%
  group_by(material, Band, Recording) %>%
  mutate(q = p.adjust(p, method="BH"),
         rob_p = r2p(rob_r, n=50))  %>%
  mutate(rob_q = p.adjust(rob_p, method="BH"))

Here is the full table of statistics:

write_csv(Rdata_ec, "correlations_channel_band_eyes_closed.csv", col_names = T)

Rdata_ec %>%
  xtable() %>%
  kable(digits = 3) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

material	Channel	Band	Recording	r	rob_r	p	q	rob_p	rob_q
Maps	AF3	Delta	Eyes Closed	0.010	-0.132	0.947	0.971	0.360	0.722
Maps	AF3	Theta	Eyes Closed	0.072	-0.060	0.621	0.790	0.681	0.953
Maps	AF3	Alpha	Eyes Closed	0.113	0.104	0.436	0.592	0.474	0.830
Maps	AF3	Low Beta	Eyes Closed	0.189	0.155	0.188	0.316	0.282	0.497
Maps	AF3	Upper Beta	Eyes Closed	0.314	0.262	0.026	0.092	0.066	0.184
Maps	AF3	High Beta	Eyes Closed	0.219	0.153	0.127	0.403	0.287	0.647
Maps	AF3	Gamma	Eyes Closed	0.101	-0.072	0.484	0.924	0.617	0.963
Maps	AF4	Delta	Eyes Closed	0.096	-0.008	0.507	0.971	0.955	0.969
Maps	AF4	Theta	Eyes Closed	0.079	-0.008	0.585	0.790	0.958	0.976
Maps	AF4	Alpha	Eyes Closed	0.147	0.128	0.308	0.582	0.374	0.760
Maps	AF4	Low Beta	Eyes Closed	0.216	0.199	0.132	0.316	0.166	0.497
Maps	AF4	Upper Beta	Eyes Closed	0.339	0.325	0.016	0.092	0.021	0.184
Maps	AF4	High Beta	Eyes Closed	0.280	0.219	0.049	0.342	0.126	0.647
Maps	AF4	Gamma	Eyes Closed	0.145	-0.007	0.315	0.924	0.963	0.963
Maps	F3	Delta	Eyes Closed	-0.005	-0.047	0.971	0.971	0.747	0.928
Maps	F3	Theta	Eyes Closed	0.072	0.004	0.619	0.790	0.976	0.976
Maps	F3	Alpha	Eyes Closed	0.140	0.089	0.333	0.582	0.540	0.839
Maps	F3	Low Beta	Eyes Closed	0.183	0.106	0.203	0.316	0.462	0.497
Maps	F3	Upper Beta	Eyes Closed	0.256	0.175	0.073	0.120	0.225	0.393
Maps	F3	High Beta	Eyes Closed	0.184	0.135	0.200	0.403	0.349	0.647
Maps	F3	Gamma	Eyes Closed	0.022	0.015	0.881	0.924	0.919	0.963
Maps	F4	Delta	Eyes Closed	0.239	0.223	0.095	0.343	0.119	0.518
Maps	F4	Theta	Eyes Closed	0.122	0.159	0.400	0.790	0.271	0.773
Maps	F4	Alpha	Eyes Closed	0.156	0.127	0.278	0.582	0.380	0.760
Maps	F4	Low Beta	Eyes Closed	0.200	0.184	0.164	0.316	0.202	0.497
Maps	F4	Upper Beta	Eyes Closed	0.290	0.265	0.041	0.116	0.063	0.184
Maps	F4	High Beta	Eyes Closed	0.215	0.217	0.133	0.403	0.130	0.647
Maps	F4	Gamma	Eyes Closed	0.135	0.099	0.349	0.924	0.493	0.963
Maps	F7	Delta	Eyes Closed	-0.058	-0.074	0.689	0.971	0.609	0.928
Maps	F7	Theta	Eyes Closed	0.087	-0.026	0.550	0.790	0.860	0.976
Maps	F7	Alpha	Eyes Closed	0.115	0.040	0.425	0.592	0.783	0.844
Maps	F7	Low Beta	Eyes Closed	0.203	0.113	0.158	0.316	0.435	0.497
Maps	F7	Upper Beta	Eyes Closed	0.254	0.149	0.075	0.120	0.301	0.468
Maps	F7	High Beta	Eyes Closed	0.121	-0.111	0.403	0.564	0.441	0.647
Maps	F7	Gamma	Eyes Closed	-0.021	-0.191	0.885	0.924	0.183	0.918
Maps	F8	Delta	Eyes Closed	0.011	-0.038	0.939	0.971	0.795	0.928
Maps	F8	Theta	Eyes Closed	0.147	0.157	0.309	0.790	0.276	0.773
Maps	F8	Alpha	Eyes Closed	0.173	0.158	0.230	0.582	0.272	0.760
Maps	F8	Low Beta	Eyes Closed	0.206	0.209	0.152	0.316	0.146	0.497
Maps	F8	Upper Beta	Eyes Closed	0.319	0.282	0.024	0.092	0.048	0.184
Maps	F8	High Beta	Eyes Closed	0.184	0.114	0.201	0.403	0.432	0.647
Maps	F8	Gamma	Eyes Closed	0.065	-0.050	0.655	0.924	0.729	0.963
Maps	FC5	Delta	Eyes Closed	-0.030	-0.041	0.838	0.971	0.776	0.928
Maps	FC5	Theta	Eyes Closed	0.025	-0.093	0.865	0.932	0.520	0.953
Maps	FC5	Alpha	Eyes Closed	0.078	-0.017	0.591	0.637	0.906	0.906
Maps	FC5	Low Beta	Eyes Closed	0.128	0.107	0.376	0.526	0.460	0.497
Maps	FC5	Upper Beta	Eyes Closed	0.153	0.079	0.288	0.367	0.587	0.632
Maps	FC5	High Beta	Eyes Closed	0.071	0.053	0.622	0.725	0.714	0.752
Maps	FC5	Gamma	Eyes Closed	0.014	0.026	0.924	0.924	0.855	0.963
Maps	FC6	Delta	Eyes Closed	0.069	0.006	0.633	0.971	0.969	0.969
Maps	FC6	Theta	Eyes Closed	0.106	0.106	0.462	0.790	0.464	0.953
Maps	FC6	Alpha	Eyes Closed	0.167	0.149	0.247	0.582	0.302	0.760
Maps	FC6	Low Beta	Eyes Closed	0.183	0.202	0.203	0.316	0.159	0.497
Maps	FC6	Upper Beta	Eyes Closed	0.272	0.296	0.056	0.120	0.037	0.184
Maps	FC6	High Beta	Eyes Closed	0.153	0.097	0.290	0.507	0.501	0.647
Maps	FC6	Gamma	Eyes Closed	0.088	-0.050	0.545	0.924	0.733	0.963
Maps	O1	Delta	Eyes Closed	0.221	0.132	0.123	0.343	0.361	0.722
Maps	O1	Theta	Eyes Closed	0.009	0.027	0.952	0.952	0.851	0.976
Maps	O1	Alpha	Eyes Closed	-0.096	-0.041	0.508	0.592	0.775	0.844
Maps	O1	Low Beta	Eyes Closed	-0.039	-0.110	0.786	0.847	0.446	0.497
Maps	O1	Upper Beta	Eyes Closed	0.100	-0.029	0.491	0.573	0.844	0.844
Maps	O1	High Beta	Eyes Closed	0.093	0.046	0.519	0.660	0.752	0.752
Maps	O1	Gamma	Eyes Closed	0.139	0.170	0.336	0.924	0.237	0.918
Maps	O2	Delta	Eyes Closed	0.229	0.191	0.109	0.343	0.185	0.518
Maps	O2	Theta	Eyes Closed	0.106	0.073	0.463	0.790	0.615	0.953
Maps	O2	Alpha	Eyes Closed	0.060	0.075	0.680	0.680	0.605	0.844
Maps	O2	Low Beta	Eyes Closed	0.098	0.117	0.497	0.632	0.419	0.497
Maps	O2	Upper Beta	Eyes Closed	0.193	0.118	0.180	0.252	0.415	0.582
Maps	O2	High Beta	Eyes Closed	0.205	0.172	0.153	0.403	0.233	0.647
Maps	O2	Gamma	Eyes Closed	0.181	0.225	0.209	0.924	0.116	0.918
Maps	P7	Delta	Eyes Closed	0.201	0.094	0.161	0.375	0.516	0.903
Maps	P7	Theta	Eyes Closed	-0.037	0.066	0.798	0.931	0.651	0.953
Maps	P7	Alpha	Eyes Closed	-0.148	0.056	0.306	0.582	0.697	0.844
Maps	P7	Low Beta	Eyes Closed	-0.028	-0.012	0.847	0.847	0.933	0.933
Maps	P7	Upper Beta	Eyes Closed	0.049	0.094	0.735	0.791	0.516	0.617
Maps	P7	High Beta	Eyes Closed	0.027	0.069	0.852	0.852	0.634	0.740
Maps	P7	Gamma	Eyes Closed	0.028	0.136	0.846	0.924	0.346	0.963
Maps	P8	Delta	Eyes Closed	0.326	0.201	0.021	0.290	0.162	0.518
Maps	P8	Theta	Eyes Closed	0.343	0.192	0.015	0.208	0.181	0.773
Maps	P8	Alpha	Eyes Closed	0.222	0.171	0.122	0.582	0.236	0.760
Maps	P8	Low Beta	Eyes Closed	0.298	0.223	0.036	0.316	0.120	0.497
Maps	P8	Upper Beta	Eyes Closed	0.365	0.196	0.009	0.092	0.171	0.343
Maps	P8	High Beta	Eyes Closed	0.332	0.115	0.018	0.258	0.427	0.647
Maps	P8	Gamma	Eyes Closed	0.288	0.162	0.043	0.599	0.262	0.918
Maps	T7	Delta	Eyes Closed	-0.042	-0.245	0.770	0.971	0.086	0.518
Maps	T7	Theta	Eyes Closed	-0.148	-0.196	0.307	0.790	0.172	0.773
Maps	T7	Alpha	Eyes Closed	-0.099	-0.184	0.494	0.592	0.200	0.760
Maps	T7	Low Beta	Eyes Closed	-0.060	-0.137	0.681	0.795	0.343	0.497
Maps	T7	Upper Beta	Eyes Closed	-0.023	-0.091	0.876	0.876	0.529	0.617
Maps	T7	High Beta	Eyes Closed	-0.037	-0.183	0.797	0.852	0.203	0.647
Maps	T7	Gamma	Eyes Closed	-0.017	-0.106	0.905	0.924	0.463	0.963
Maps	T8	Delta	Eyes Closed	0.247	0.310	0.084	0.343	0.028	0.397
Maps	T8	Theta	Eyes Closed	0.251	0.291	0.078	0.547	0.041	0.570
Maps	T8	Alpha	Eyes Closed	0.239	0.227	0.095	0.582	0.113	0.760
Maps	T8	Low Beta	Eyes Closed	0.214	0.177	0.135	0.316	0.218	0.497
Maps	T8	Upper Beta	Eyes Closed	0.252	0.217	0.077	0.120	0.130	0.303
Maps	T8	High Beta	Eyes Closed	0.140	0.096	0.332	0.517	0.508	0.647
Maps	T8	Gamma	Eyes Closed	0.036	-0.007	0.804	0.924	0.962	0.963
Vocabulary	AF3	Delta	Eyes Closed	-0.008	-0.149	0.958	0.958	0.303	0.707
Vocabulary	AF3	Theta	Eyes Closed	0.023	-0.040	0.873	0.962	0.784	0.989
Vocabulary	AF3	Alpha	Eyes Closed	0.086	-0.016	0.553	0.779	0.914	0.934
Vocabulary	AF3	Low Beta	Eyes Closed	0.298	0.181	0.035	0.145	0.208	0.450
Vocabulary	AF3	Upper Beta	Eyes Closed	0.225	0.126	0.116	0.334	0.385	0.794
Vocabulary	AF3	High Beta	Eyes Closed	0.020	-0.046	0.890	0.958	0.753	0.832
Vocabulary	AF3	Gamma	Eyes Closed	-0.091	-0.155	0.529	0.617	0.281	0.662
Vocabulary	AF4	Delta	Eyes Closed	-0.059	-0.176	0.683	0.939	0.221	0.707
Vocabulary	AF4	Theta	Eyes Closed	0.036	-0.030	0.802	0.962	0.838	0.989
Vocabulary	AF4	Alpha	Eyes Closed	0.105	0.040	0.466	0.779	0.783	0.934
Vocabulary	AF4	Low Beta	Eyes Closed	0.257	0.144	0.071	0.157	0.319	0.450
Vocabulary	AF4	Upper Beta	Eyes Closed	0.223	0.114	0.119	0.334	0.430	0.794
Vocabulary	AF4	High Beta	Eyes Closed	0.026	-0.089	0.859	0.958	0.537	0.832
Vocabulary	AF4	Gamma	Eyes Closed	-0.114	-0.142	0.432	0.605	0.324	0.662
Vocabulary	F3	Delta	Eyes Closed	-0.201	-0.230	0.162	0.939	0.108	0.707
Vocabulary	F3	Theta	Eyes Closed	-0.007	-0.008	0.962	0.962	0.956	0.989
Vocabulary	F3	Alpha	Eyes Closed	0.064	0.020	0.660	0.840	0.891	0.934
Vocabulary	F3	Low Beta	Eyes Closed	0.235	0.209	0.101	0.157	0.145	0.450
Vocabulary	F3	Upper Beta	Eyes Closed	0.175	0.029	0.223	0.428	0.840	0.900
Vocabulary	F3	High Beta	Eyes Closed	-0.035	-0.027	0.808	0.958	0.853	0.853
Vocabulary	F3	Gamma	Eyes Closed	-0.191	-0.028	0.185	0.581	0.847	0.894
Vocabulary	F4	Delta	Eyes Closed	-0.041	-0.061	0.778	0.939	0.674	0.875
Vocabulary	F4	Theta	Eyes Closed	0.036	0.011	0.802	0.962	0.938	0.989
Vocabulary	F4	Alpha	Eyes Closed	0.105	0.060	0.469	0.779	0.680	0.934
Vocabulary	F4	Low Beta	Eyes Closed	0.273	0.162	0.055	0.154	0.261	0.450
Vocabulary	F4	Upper Beta	Eyes Closed	0.249	0.119	0.082	0.334	0.412	0.794
Vocabulary	F4	High Beta	Eyes Closed	-0.001	-0.054	0.997	0.997	0.709	0.832
Vocabulary	F4	Gamma	Eyes Closed	-0.140	-0.019	0.332	0.581	0.894	0.894
Vocabulary	F7	Delta	Eyes Closed	0.015	-0.023	0.917	0.958	0.875	0.875
Vocabulary	F7	Theta	Eyes Closed	0.029	0.046	0.842	0.962	0.753	0.989
Vocabulary	F7	Alpha	Eyes Closed	0.099	0.019	0.494	0.779	0.893	0.934
Vocabulary	F7	Low Beta	Eyes Closed	0.290	0.230	0.041	0.145	0.108	0.450
Vocabulary	F7	Upper Beta	Eyes Closed	0.168	0.113	0.245	0.428	0.433	0.794
Vocabulary	F7	High Beta	Eyes Closed	-0.043	-0.069	0.765	0.958	0.634	0.832
Vocabulary	F7	Gamma	Eyes Closed	-0.126	-0.160	0.383	0.595	0.268	0.662
Vocabulary	F8	Delta	Eyes Closed	-0.126	-0.115	0.383	0.939	0.426	0.852
Vocabulary	F8	Theta	Eyes Closed	-0.019	-0.043	0.897	0.962	0.768	0.989
Vocabulary	F8	Alpha	Eyes Closed	0.137	0.041	0.343	0.779	0.779	0.934
Vocabulary	F8	Low Beta	Eyes Closed	0.241	0.151	0.091	0.157	0.294	0.450
Vocabulary	F8	Upper Beta	Eyes Closed	0.196	0.100	0.173	0.403	0.490	0.794
Vocabulary	F8	High Beta	Eyes Closed	-0.082	-0.042	0.571	0.958	0.773	0.832
Vocabulary	F8	Gamma	Eyes Closed	-0.206	-0.167	0.151	0.581	0.246	0.662
Vocabulary	FC5	Delta	Eyes Closed	-0.087	-0.215	0.546	0.939	0.135	0.707
Vocabulary	FC5	Theta	Eyes Closed	-0.090	-0.189	0.532	0.962	0.189	0.989
Vocabulary	FC5	Alpha	Eyes Closed	-0.007	-0.166	0.962	0.962	0.248	0.934
Vocabulary	FC5	Low Beta	Eyes Closed	0.148	0.143	0.306	0.357	0.321	0.450
Vocabulary	FC5	Upper Beta	Eyes Closed	0.046	-0.042	0.751	0.809	0.773	0.900
Vocabulary	FC5	High Beta	Eyes Closed	-0.115	-0.082	0.428	0.958	0.573	0.832
Vocabulary	FC5	Gamma	Eyes Closed	-0.150	-0.137	0.297	0.581	0.343	0.662
Vocabulary	FC6	Delta	Eyes Closed	-0.092	-0.162	0.524	0.939	0.261	0.707
Vocabulary	FC6	Theta	Eyes Closed	-0.040	-0.071	0.781	0.962	0.625	0.989
Vocabulary	FC6	Alpha	Eyes Closed	0.097	0.074	0.501	0.779	0.608	0.934
Vocabulary	FC6	Low Beta	Eyes Closed	0.203	0.173	0.157	0.214	0.229	0.450
Vocabulary	FC6	Upper Beta	Eyes Closed	0.154	0.111	0.286	0.444	0.444	0.794
Vocabulary	FC6	High Beta	Eyes Closed	-0.084	-0.097	0.562	0.958	0.502	0.832
Vocabulary	FC6	Gamma	Eyes Closed	-0.172	-0.094	0.232	0.581	0.517	0.695
Vocabulary	O1	Delta	Eyes Closed	-0.044	-0.065	0.759	0.939	0.653	0.875
Vocabulary	O1	Theta	Eyes Closed	0.057	-0.077	0.697	0.962	0.593	0.989
Vocabulary	O1	Alpha	Eyes Closed	0.041	-0.051	0.776	0.866	0.726	0.934
Vocabulary	O1	Low Beta	Eyes Closed	0.198	0.089	0.168	0.214	0.538	0.622
Vocabulary	O1	Upper Beta	Eyes Closed	0.138	-0.126	0.340	0.476	0.382	0.794
Vocabulary	O1	High Beta	Eyes Closed	-0.052	-0.279	0.722	0.958	0.050	0.698
Vocabulary	O1	Gamma	Eyes Closed	-0.145	-0.127	0.315	0.581	0.378	0.662
Vocabulary	O2	Delta	Eyes Closed	-0.036	-0.028	0.804	0.939	0.845	0.875
Vocabulary	O2	Theta	Eyes Closed	0.077	0.079	0.597	0.962	0.584	0.989
Vocabulary	O2	Alpha	Eyes Closed	0.122	0.024	0.399	0.779	0.869	0.934
Vocabulary	O2	Low Beta	Eyes Closed	0.340	0.269	0.016	0.111	0.059	0.412
Vocabulary	O2	Upper Beta	Eyes Closed	0.271	0.045	0.057	0.334	0.756	0.900
Vocabulary	O2	High Beta	Eyes Closed	0.071	-0.146	0.625	0.958	0.313	0.832
Vocabulary	O2	Gamma	Eyes Closed	-0.092	-0.087	0.524	0.617	0.546	0.695
Vocabulary	P7	Delta	Eyes Closed	-0.065	-0.068	0.656	0.939	0.640	0.875
Vocabulary	P7	Theta	Eyes Closed	-0.121	-0.094	0.404	0.962	0.514	0.989
Vocabulary	P7	Alpha	Eyes Closed	-0.085	0.012	0.556	0.779	0.934	0.934
Vocabulary	P7	Low Beta	Eyes Closed	0.040	-0.053	0.783	0.843	0.716	0.716
Vocabulary	P7	Upper Beta	Eyes Closed	-0.020	-0.087	0.892	0.892	0.547	0.794
Vocabulary	P7	High Beta	Eyes Closed	-0.119	-0.132	0.410	0.958	0.360	0.832
Vocabulary	P7	Gamma	Eyes Closed	-0.184	-0.158	0.201	0.581	0.273	0.662
Vocabulary	P8	Delta	Eyes Closed	0.072	-0.034	0.620	0.939	0.813	0.875
Vocabulary	P8	Theta	Eyes Closed	0.226	0.097	0.115	0.962	0.503	0.989
Vocabulary	P8	Alpha	Eyes Closed	0.210	0.093	0.142	0.779	0.523	0.934
Vocabulary	P8	Low Beta	Eyes Closed	0.424	0.356	0.002	0.030	0.011	0.156
Vocabulary	P8	Upper Beta	Eyes Closed	0.335	0.138	0.017	0.242	0.341	0.794
Vocabulary	P8	High Beta	Eyes Closed	0.158	-0.117	0.273	0.958	0.417	0.832
Vocabulary	P8	Gamma	Eyes Closed	0.013	-0.104	0.926	0.926	0.473	0.695
Vocabulary	T7	Delta	Eyes Closed	-0.051	-0.201	0.726	0.939	0.161	0.707
Vocabulary	T7	Theta	Eyes Closed	-0.104	-0.252	0.471	0.962	0.077	0.989
Vocabulary	T7	Alpha	Eyes Closed	0.036	-0.126	0.804	0.866	0.385	0.934
Vocabulary	T7	Low Beta	Eyes Closed	-0.027	-0.081	0.851	0.851	0.577	0.622
Vocabulary	T7	Upper Beta	Eyes Closed	-0.059	-0.083	0.684	0.798	0.567	0.794
Vocabulary	T7	High Beta	Eyes Closed	-0.058	-0.174	0.690	0.958	0.227	0.832
Vocabulary	T7	Gamma	Eyes Closed	-0.064	-0.205	0.660	0.710	0.152	0.662
Vocabulary	T8	Delta	Eyes Closed	-0.037	-0.026	0.798	0.939	0.855	0.875
Vocabulary	T8	Theta	Eyes Closed	0.041	0.002	0.779	0.962	0.989	0.989
Vocabulary	T8	Alpha	Eyes Closed	0.173	0.131	0.230	0.779	0.363	0.934
Vocabulary	T8	Low Beta	Eyes Closed	0.238	0.108	0.096	0.157	0.455	0.580
Vocabulary	T8	Upper Beta	Eyes Closed	0.097	-0.018	0.504	0.641	0.900	0.900
Vocabulary	T8	High Beta	Eyes Closed	-0.096	-0.124	0.506	0.958	0.392	0.832
Vocabulary	T8	Gamma	Eyes Closed	-0.225	-0.043	0.116	0.581	0.769	0.894

Topological Maps

We can visualize the topological distributions of the correlations on a canonical EEG headmodel. To do so, we need to prepare the data tibble by addinge electrode information using the eegUtils package.

Rdata_ec <- Rdata_ec %>% 
  mutate(electrode = Channel)
Rdata_ec <- electrode_locations(Rdata_ec)

Now we can visualize the results

if (!"Significance" %in% names(Rdata_ec)) {
  Rdata_ec <- Rdata_ec %>%
    add_column(Significance = "p > .05")
}
Rdata_ec$Significance[Rdata_ec$p < 0.05] <- "p < .05"
Rdata_ec$Significance[Rdata_ec$q < 0.05] <- "q < .05"
Rdata_ec$Significance <- factor(Rdata_ec$Significance, 
                                levels=c("p > .05", "p < .05", "q < .05"))

Rdata_ec_sub <- Rdata_ec %>% 
  filter(Band %in% c("Theta", "Alpha", "Low Beta", "Upper Beta", "High Beta"))

ggplot(Rdata_ec_sub, aes(x = x,
                         y = y,
                         fill = r,
                         z = r,
                         label = Channel)) +
  scale_fill_distiller(palette = "RdBu",
                       limits=c(-0.45, 0.45)) +
  theme_void() +
  coord_equal() +
  facet_grid(material~Band, switch="both") +
  #labs(fill = expression(paste("Correlation with ", alpha))) +
  labs(fill = "Correlation") +
  stat_scalpmap(grid_res = 200,
                interp_limit = "skirt",  # head or skirt
                method = "biharmonic")+
  #geom_mask(scale_fac = 1.6) +
  geom_head(color="black") +
  geom_channels(geom = "text",
                size = 3,
                vjust = 1.5
  ) +
  geom_channels(geom = "point",
                size = 2 ,
                aes(col = Significance)) +
  scale_color_manual(values = c("black", 
                                "purple", 
                                "gold")) +
  ggtitle(expression(paste("Correlations Between Spectral Power and ", 
                           alpha, 
                           ", Eyes Closed"))) +
  theme(plot.title = element_text(hjust = 0.5))

ggsave("images/topo_ec.pdf", width=9, height = 3.5)
ggsave("images/figure3.png", width=9, height = 3.5, dpi=300)

Distribution of r and p values

And here is the corresponding distributions of r and p values. The dashed lines correspond to a significant threshold of p < .05 on either a r or a p-value scale.

ggplot(Rdata_ec, aes(x = Band, y = r, col = Channel)) +
  geom_point() +
  stat_summary(fun.data = "mean_se", col="black", 
               alpha=0.5, geom = "errorbar") +
  facet_wrap(~ material) +
  ggtitle("Correlation by Frequency Band") +
  ylab("r value") +
  annotate("segment", x=-Inf, xend=Inf, y=0.28, yend = 0.28, lty=2) +
  annotate("segment", x=-Inf, xend=Inf, y=-0.28, yend = -0.28, lty=2) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  theme_pander()

ggplot(Rdata_ec, aes(x = Band, y = p, col = Channel)) +
  geom_point() +
  stat_summary(fun.data = "mean_se", col="black", alpha=0.5, geom = "errorbar") +
  ggtitle("p-value, by Band") +
  ylab("p value") +
  facet_wrap(~ material) +
  annotate("segment", x=-Inf, xend=Inf, y=0.05, yend = 0.05, lty=2) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  scale_y_log10() +
  theme_pander()

From the original list, we can extract only those channels that survive the FDR correction:

survivors <- Rdata_ec %>%
  filter(rob_q < 0.05 | q < 0.05)
  
survivors %>%
  xtable() %>%
  kable(digits = 3) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

material	Channel	Band	Recording	r	rob_r	p	q	rob_p	rob_q	electrode	radius	theta	phi	cart_x	cart_y	cart_z	x	y	Significance
Vocabulary	P8	Low Beta	Eyes Closed	0.424	0.356	0.002	0.03	0.011	0.156	P8	1	91	-45	73.06	-73.07	-2.54	64.35	-64.35	q < .05

Only one channel P8, in one frequency band (low beta) survives FDR correction. We can plot the correlation between power in P8 and rate of forgetting to see the relationship:

focus <- Fdata_ec %>%
  filter(material == "Vocabulary", 
         Channel %in% survivors$Channel,
         Band == "Low Beta") %>%
  rename(Alpha = alpha)

p <- ggplot(focus, aes(x = Alpha, y = Power)) +
  geom_point(size = 4, alpha = 0.5, col = "black") + 
  geom_smooth(method = "lm", formula = y ~ x, 
              col="red", fill="red", fullrange = T, lwd=2) +
  theme_pander() +
  scale_x_continuous() +   
  scale_y_continuous() + 
  ggtitle("Rate of Forgetting\nand Eyes-Closed Beta Power") +
  xlab(expression(paste(alpha, " Vocabulary"))) +
  ylab("Low Beta (13-15 Hz) Power Over P8") +
  geom_text(data=survivors, col="red",
            mapping=aes(x=0.25, y=10.5, 
                        label= paste("r =", round(r, 2)))) +
  theme(plot.title = element_text(hjust = 0.5))

ggMarginal(p, col = "white", fill = "black", alpha = 0.5,
           type = "hist", bins = 13)

ggsave("images/figure4.png")

## Saving 5 x 5 in image

Eyes-Open EEG Data

(Optional) Preprocess the Data

Set the process_raw variable to TRUE to reprocess the raw EEG data. That might take a very long time.

process_raw = F
if (process_raw) {
  setwd("eyes_open/")
  for (sub in dir(grep("A[1-9]", dir()))) {
    setwd(sub)
    analyze.logfile(sub, "open")
    setwd("..")
  }
  setwd("..")
}

Load the Eyes-Open Spectra and Summary Files

As in the case of eyes-closed data, we are going to examine the spectral and IAF characteristics of eyes-open recordings though the summary files produced by preprocessing.

eo_spectra <- NULL
for (sub in dir("data/eyes_open/")[grep("A[1-9]", 
                                        dir("data/eyes_open/"))]) {
  table <- read_tsv(paste("data/eyes_open/", sub, "/", 
                          sub, "_open_spectra.txt", sep=""),
                    col_types = cols())
  if (is.null(eo_spectra)) {
    eo_spectra <- table
  } else {
    eo_spectra <- eo_spectra %>% bind_rows(table)
  }
}

eo_summary <- NULL
for (sub in dir("data/eyes_open/")[grep("A[1-9]", 
                                        dir("data/eyes_open/"))]) {
  table <- read_tsv(paste("data/eyes_open/", sub, "/", 
                          sub, "_open_summary.txt", sep=""),
                    col_types = cols())
  if (is.null(eo_summary)) {
    eo_summary <- table
  } else {
    eo_summary <- eo_summary %>% bind_rows(table)
  }
}

Individual Alpha frquency

Since eyes-open recordings are somewhat less common, we need to run a few sanity checks on them. The first and most obsvious concerns the IAF, and whether the IAFs during eyes-open recordings are similar and correlated to those observed during eyes-closed sessions.

eo_iafs <- eo_summary %>%
  select(c("Subject", iaf_cols)) %>%
  filter(Subject %in% wbehav$Subject) %>%
  pivot_longer(cols = iaf_cols, names_sep="_", 
               names_to = c("Channel", "Discard"), 
               values_to = "IAF_EO") %>%
  select(Subject, Channel, IAF_EO) %>%
  group_by(Subject) %>%
  summarize(IAF_EO = Mode(IAF_EO))

iafs <- inner_join(eo_iafs, ec_iafs, by="Subject")

The mean IAF thus calculated is 10.33, which is, once more, exactly in the middle of our alpha frequency band.

ggplot(data=eo_iafs, aes(x = IAF_EO)) +
  geom_histogram(aes(col="white"), fill="black", 
                 colour="white", alpha=0.5, 
                 position="identity", binwidth = 0.5) +
  ggtitle("IAF Distribution, Eyes-Open") + 
  xlab("IAF (Eyes Open)") +
  theme_pander()

As it can be seen, there is a significant correlation in the IAFs between the two recordings (p = 10^{-6}). Between recordings, the individual IAF remains the same +/- 0.83.

iafplot <- ggplot(iafs, aes(x=IAF_EO, y=IAF_EC)) +
  geom_count(alpha=0.5, col="black") + 
  scale_size_area() +
  geom_smooth(method = "lm", formula = y ~ x, 
              col="red", fill="red", fullrange = T, lwd=2) +
  xlab("IAF Eyes Open (Hz)") +
  ylab("IAF Eyes Closed (Hz") +
  geom_text(data = summarize(iafs, Mean=mean(IAF_EC)),  # Avoid label overlaps
            x= 9.5, y=11.5, 
            col="red", 
            label=paste("r =", 
                        round(cor(iafs$IAF_EC, iafs$IAF_EO), 2))) +
  ggtitle("IAF Across Recordings") +
  theme_pander()

iafplot

Average Spectrum

We can also investigate whether the average spectrum is comparable to that of eyes-closed recordings. To do so, we will calculate the average spectrum across channels, much in the same ways as it was done for eyes-closed data:

l_eo_spectra <- pivot_longer(eo_spectra, cols=names(ec_spectra)[3:130], 
                         names_to="Freq")

l_eo_spectra <- l_eo_spectra %>% 
  rename(Power = value) %>%
  add_column(Recording = "Eyes Open")

l_eo_spectra$Frequency <- as.numeric(substr(l_eo_spectra$Freq, 
                                            0, str_length(l_eo_spectra$Freq) -2))
l_eo_spectra <- l_eo_spectra %>% 
  add_column(Band="Delta", BandMin=0, BandMax=4) 
l_eo_spectra$Band[l_eo_spectra$Frequency <= 40] <- "Gamma"
l_eo_spectra$Band[l_eo_spectra$Frequency < 30] <- "High Beta"
l_eo_spectra$Band[l_eo_spectra$Frequency < 18] <- "Upper Beta"
l_eo_spectra$Band[l_eo_spectra$Frequency < 15] <- "Low Beta"
l_eo_spectra$Band[l_eo_spectra$Frequency < 13] <- "Alpha"
l_eo_spectra$Band[l_eo_spectra$Frequency < 8] <- "Theta"
l_eo_spectra$Band[l_eo_spectra$Frequency < 4] <- "Delta"

l_eo_spectra$BandMin[l_eo_spectra$Frequency <= 40] <- 30
l_eo_spectra$BandMin[l_eo_spectra$Frequency < 30] <- 18
l_eo_spectra$BandMin[l_eo_spectra$Frequency < 18] <- 15
l_eo_spectra$BandMin[l_eo_spectra$Frequency < 15] <- 13
l_eo_spectra$BandMin[l_eo_spectra$Frequency < 13] <- 8
l_eo_spectra$BandMin[l_eo_spectra$Frequency < 8] <- 4
l_eo_spectra$BandMin[l_eo_spectra$Frequency < 4] <- 0

l_eo_spectra$BandMax[l_eo_spectra$Frequency <= 40] <- 40
l_eo_spectra$BandMax[l_eo_spectra$Frequency < 30] <- 30
l_eo_spectra$BandMax[l_eo_spectra$Frequency < 18] <- 18
l_eo_spectra$BandMax[l_eo_spectra$Frequency < 15] <- 15
l_eo_spectra$BandMax[l_eo_spectra$Frequency < 13] <- 13
l_eo_spectra$BandMax[l_eo_spectra$Frequency < 8] <- 8
l_eo_spectra$BandMax[l_eo_spectra$Frequency < 4] <- 4

l_eo_spectra$Band <- factor(l_eo_spectra$Band, 
                        levels = c("Delta", "Theta", "Alpha", 
                                   "Low Beta", "Upper Beta",
                                   "High Beta", "Gamma"))

Now, we remove the three participants for which we have poor quality data

l_eo_spectra <- l_eo_spectra %>%
  filter(Subject %in% behav$Subject)

And we can visualize the eyes-open power spectra by channel:

gd <- l_eo_spectra %>% 
        group_by(Band) %>% 
        summarise(
          Min = mean(BandMin),
          Max = mean(BandMax),
          Power =mean(Power),
          Frequency = mean(BandMin)
        )

ggplot(data=l_eo_spectra, aes(x=Frequency, y=Power, Channel)) +
  geom_rect(data = gd, aes(xmin = Min, xmax = Max, fill = Band), 
            ymin=0, ymax=Inf, colour=NA, alpha=0.5) +
  stat_summary(fun.data=mean_sdl, 
               geom = "ribbon", colour = "white", alpha = 0.4) +
  stat_summary(fun = mean, geom = "line", lwd = 1) +
  facet_wrap(~ Channel, ncol=4) +
  scale_alpha_manual(values = seq(0.1, 0.9, 0.1)) +
  ggtitle("Eyes-Open Power Spectrum Across Channels") +
  ylab("Log Power") + 
  xlab("Frequency (Hz)") + 
  theme_pander() +
  coord_cartesian(xlim=c(1,40), ylim=c(5, 17)) + #ylim=c(5,17)) +
  #scale_fill_brewer(palette = "Set3") +
  scale_fill_jco() +
  theme(plot.title = element_text(hjust = 0.5))

At a first sight, the eyes-open power spectrum looks remarkably similar, with the obvious (and expected) difference of a less prominent pean in the alpha band. To better compare the two types of recordings, we can visualize the avearage spectra on top of ech other:

l_spectra <- rbind(l_ec_spectra, l_eo_spectra)

al_spectra <- l_spectra %>%
  group_by(Subject, Frequency, Band, Recording) %>%
  summarise(Power = mean(Power))

## `summarise()` has grouped output by 'Subject', 'Frequency', 'Band'. You can override using the `.groups` argument.

al_spectra <- al_spectra %>% ungroup()

ggplot(data=al_spectra, aes(x = Frequency, y = Power, 
                            col = Recording, fill = Recording)) +
  annotate("rect", xmin = 0, xmax = 4, ymin = 0, ymax = Inf, 
           alpha = 0.4, fill=K[1]) +
  annotate("rect", xmin = 4, xmax = 8, ymin = 0, ymax = Inf, 
           alpha = 0.4, fill=K[2]) +
  annotate("rect", xmin = 8, xmax = 13, ymin = 0, ymax = Inf, 
           alpha = 0.4, fill=K[3]) +      
  annotate("rect", xmin = 13, xmax = 15, ymin = 0, ymax = Inf, 
           alpha = 0.4, fill=K[4]) +
  annotate("rect", xmin = 15, xmax = 18, ymin = 0, ymax = Inf, 
           alpha = 0.4, fill = K[5]) +
  annotate("rect", xmin = 18, xmax = 30, ymin = 0, ymax = Inf, 
           alpha = 0.4, fill=K[6]) +
  annotate("rect", xmin = 30, xmax = 40, ymin = 0, ymax = Inf,
           alpha = 0.4, fill=K[7]) +
  stat_summary(fun.data = mean_sdl, geom = "ribbon", 
               alpha = 0.5, col = "white") +
  scale_color_brewer(palette = "Set1") +
  scale_fill_brewer(palette = "Set1") +
  stat_summary(fun = mean, geom = "line", lwd = 2) +
  coord_cartesian(xlim=c(0,40), ylim=c(5,18)) +
  xlab("Frequency (Hz)") +
  ylab("Log Power") +
  theme_pander() +
  annotate("text", x=2, y=18, label="Delta", angle=90, hjust=1) +
  annotate("text", x=6, y=18, label="Theta", angle=90, hjust=1) +
  annotate("text", x=10.5, y=18, label="Alpha", angle=90, hjust=1) +
  annotate("text", x=14, y=18, label="Low Beta", angle=90, hjust=1) +
  annotate("text", x=16.5, y=18, label="Upper Beta", angle=90, hjust=1) +
  annotate("text", x=24, y=18, label="High Beta", angle=90, hjust=1) +
  annotate("text", x=35, y=18, label="Gamma", angle=90, hjust=1) 

ggsave("images/figure2.png", dpi=300)

## Saving 6 x 3 in image

Correlations Across Eyes Open and Eyes Closed Recordings

test <- al_spectra %>%
  group_by(Subject, Band, Recording) %>%
  summarize(Power = mean(Power)) %>%
  ungroup()

## `summarise()` has grouped output by 'Subject', 'Band'. You can override using the `.groups` argument.

test$Recording <- recode(test$Recording, 
                         `Eyes Open` = "EyesOpen", 
                         `Eyes Closed` = "EyesClosed")

by_subj <- test %>%
  pivot_wider(id_cols = c("Subject", "Band"), 
              values_from = c("Power"), 
              names_from = c("Recording")) 

global <- by_subj %>%
  group_by(Band) %>%
  summarize(r=cor(EyesOpen, EyesClosed), 
            peo=mean(EyesOpen),
            pec=mean(EyesClosed))

global$y <- seq(8,11, 0.5)

ggplot(global, aes(x=Band, y=r, fill=Band, color=Band)) +
  geom_bar(stat="identity", alpha=0.75) +
  scale_fill_jco() +
  scale_color_jco() +
  ylim(0,1) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
  theme_pander() 

And we can actually compare the correlations between IAF and between spectral powers across different bands.

bandplot <- ggplot(by_subj, aes(y=EyesClosed, x=EyesOpen, fill=Band, color=Band)) +
  geom_point(stat="identity") +
  geom_smooth(method="lm") +
  scale_color_jco() +
  scale_fill_jco() +
  xlab("Power Eyes Open") +
  ylab("Power Eyes Closed") +
  geom_text(data = global, 
            x = global$peo + 1.5, y=global$pec -.5,
            label=paste("r =", 
                        round(global$r, 2)),
            show.legend = F) +
  ggtitle("(B) Spectral Power Across Recordings") +
  theme_pander() +
  theme(legend.position="bottom") 


iafplot <- iafplot + theme(legend.position="bottom") +   
  ggtitle("(A) IAF Across Recordings") 

grid.arrange(iafplot, bandplot, ncol=2)

## `geom_smooth()` using formula 'y ~ x'

g <- arrangeGrob(iafplot, bandplot, ncol=2) #generates g

## `geom_smooth()` using formula 'y ~ x'

ggsave(file="figure5ab.png", g, dpi=300, 
       width = 9, height=5, units = "in")

Correlations with Rate of Forgetting

Like we did for eyes-closed data, we can now calculate the correlations between eyes-open EEG power spectra and the behavioral rate fo forgetting for verbal and visual materials.

wbehav <- wbehav %>%
  mutate(GlobalAlpha=(Vocabulary + Maps) / 2)

Adata_eo <- l_eo_spectra %>% 
  group_by(Subject, Channel, Band, Recording) %>% 
  summarize(Power=mean(Power))

## `summarise()` has grouped output by 'Subject', 'Channel', 'Band'. You can override using the `.groups` argument.

Fdata_eo <- inner_join(Adata_eo, behav, by = "Subject")

Cdata_eo <- Fdata_eo %>%
  group_by(material, Channel, Band, Recording) %>%
  summarise(r = cor(Power, alpha),
            p = cor.test(Power, alpha)$p.value,
            rob_r = robcor(Power, alpha))

## `summarise()` has grouped output by 'material', 'Channel', 'Band'. You can override using the `.groups` argument.

Rdata_eo <- Cdata_eo %>%
  group_by(material, Band, Recording) %>%
  mutate(q = p.adjust(p, method="BH")) %>%
  mutate(rob_p = r2p(rob_r, n=50)) %>%
  mutate(rob_q = p.adjust(rob_p, method="BH"))

We save the data so that it can be visualized as a topomap with Python MNE. Here is the full table of statistics:

write_csv(Rdata_eo, "correlations_channel_band_eyes_open.csv",
          col_names = T)

Rdata_eo %>%
  xtable() %>%
  kable(digits = 3) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

material	Channel	Band	Recording	r	p	rob_r	q	rob_p	rob_q
Maps	AF3	Delta	Eyes Open	0.180	0.211	0.111	0.686	0.442	0.998
Maps	AF3	Theta	Eyes Open	0.122	0.397	0.097	0.556	0.501	0.842
Maps	AF3	Alpha	Eyes Open	0.218	0.129	0.145	0.200	0.316	0.492
Maps	AF3	Low Beta	Eyes Open	0.255	0.074	0.249	0.206	0.082	0.246
Maps	AF3	Upper Beta	Eyes Open	0.383	0.006	0.389	0.021	0.005	0.029
Maps	AF3	High Beta	Eyes Open	0.279	0.050	0.336	0.140	0.017	0.059
Maps	AF3	Gamma	Eyes Open	0.236	0.100	0.287	0.447	0.043	0.203
Maps	AF4	Delta	Eyes Open	0.141	0.330	0.098	0.686	0.500	0.998
Maps	AF4	Theta	Eyes Open	0.136	0.347	0.158	0.540	0.273	0.842
Maps	AF4	Alpha	Eyes Open	0.247	0.083	0.185	0.184	0.197	0.391
Maps	AF4	Low Beta	Eyes Open	0.301	0.034	0.273	0.206	0.055	0.246
Maps	AF4	Upper Beta	Eyes Open	0.383	0.006	0.403	0.021	0.004	0.029
Maps	AF4	High Beta	Eyes Open	0.315	0.026	0.417	0.140	0.003	0.036
Maps	AF4	Gamma	Eyes Open	0.256	0.073	0.300	0.447	0.034	0.203
Maps	F3	Delta	Eyes Open	0.065	0.656	-0.181	0.707	0.207	0.968
Maps	F3	Theta	Eyes Open	0.246	0.085	0.011	0.441	0.940	0.975
Maps	F3	Alpha	Eyes Open	0.232	0.105	0.111	0.184	0.443	0.621
Maps	F3	Low Beta	Eyes Open	0.221	0.122	0.133	0.244	0.357	0.412
Maps	F3	Upper Beta	Eyes Open	0.326	0.021	0.292	0.037	0.040	0.070
Maps	F3	High Beta	Eyes Open	0.219	0.126	0.137	0.253	0.344	0.438
Maps	F3	Gamma	Eyes Open	0.126	0.384	0.099	0.662	0.495	0.693
Maps	F4	Delta	Eyes Open	0.103	0.478	-0.044	0.686	0.763	0.998
Maps	F4	Theta	Eyes Open	0.236	0.099	0.180	0.441	0.212	0.842
Maps	F4	Alpha	Eyes Open	0.248	0.082	0.206	0.184	0.151	0.352
Maps	F4	Low Beta	Eyes Open	0.265	0.063	0.235	0.206	0.101	0.246
Maps	F4	Upper Beta	Eyes Open	0.338	0.016	0.338	0.034	0.016	0.038
Maps	F4	High Beta	Eyes Open	0.294	0.038	0.355	0.140	0.011	0.053
Maps	F4	Gamma	Eyes Open	0.204	0.154	0.191	0.447	0.184	0.608
Maps	F7	Delta	Eyes Open	0.233	0.104	0.144	0.686	0.319	0.998
Maps	F7	Theta	Eyes Open	0.148	0.304	0.052	0.540	0.722	0.842
Maps	F7	Alpha	Eyes Open	0.247	0.084	0.175	0.184	0.224	0.391
Maps	F7	Low Beta	Eyes Open	0.281	0.048	0.151	0.206	0.296	0.391
Maps	F7	Upper Beta	Eyes Open	0.428	0.002	0.357	0.013	0.011	0.030
Maps	F7	High Beta	Eyes Open	0.279	0.050	0.190	0.140	0.186	0.371
Maps	F7	Gamma	Eyes Open	0.202	0.160	0.119	0.447	0.411	0.693
Maps	F8	Delta	Eyes Open	0.071	0.624	0.030	0.707	0.834	0.998
Maps	F8	Theta	Eyes Open	0.175	0.225	0.062	0.526	0.668	0.842
Maps	F8	Alpha	Eyes Open	0.287	0.043	0.241	0.184	0.091	0.352
Maps	F8	Low Beta	Eyes Open	0.242	0.091	0.240	0.212	0.093	0.246
Maps	F8	Upper Beta	Eyes Open	0.336	0.017	0.317	0.034	0.025	0.050
Maps	F8	High Beta	Eyes Open	0.199	0.166	0.178	0.291	0.217	0.380
Maps	F8	Gamma	Eyes Open	0.088	0.544	0.012	0.662	0.932	0.932
Maps	FC5	Delta	Eyes Open	0.032	0.826	0.094	0.826	0.517	0.998
Maps	FC5	Theta	Eyes Open	-0.028	0.845	0.005	0.985	0.975	0.975
Maps	FC5	Alpha	Eyes Open	0.167	0.247	0.057	0.315	0.693	0.693
Maps	FC5	Low Beta	Eyes Open	0.189	0.188	0.147	0.263	0.307	0.391
Maps	FC5	Upper Beta	Eyes Open	0.229	0.109	0.245	0.153	0.087	0.110
Maps	FC5	High Beta	Eyes Open	0.137	0.343	0.054	0.403	0.710	0.710
Maps	FC5	Gamma	Eyes Open	0.094	0.515	-0.072	0.662	0.621	0.724
Maps	FC6	Delta	Eyes Open	-0.115	0.425	-0.281	0.686	0.048	0.599
Maps	FC6	Theta	Eyes Open	0.094	0.515	-0.073	0.655	0.614	0.842
Maps	FC6	Alpha	Eyes Open	0.190	0.187	0.216	0.261	0.131	0.352
Maps	FC6	Low Beta	Eyes Open	0.132	0.362	0.218	0.461	0.128	0.255
Maps	FC6	Upper Beta	Eyes Open	0.179	0.214	0.268	0.249	0.060	0.093
Maps	FC6	High Beta	Eyes Open	0.136	0.346	0.148	0.403	0.303	0.438
Maps	FC6	Gamma	Eyes Open	0.037	0.798	0.012	0.798	0.932	0.932
Maps	O1	Delta	Eyes Open	0.074	0.608	-0.014	0.707	0.920	0.998
Maps	O1	Theta	Eyes Open	0.011	0.938	-0.106	0.988	0.465	0.842
Maps	O1	Alpha	Eyes Open	0.085	0.556	0.081	0.649	0.577	0.693
Maps	O1	Low Beta	Eyes Open	0.026	0.857	-0.126	0.857	0.383	0.412
Maps	O1	Upper Beta	Eyes Open	0.191	0.183	0.193	0.233	0.180	0.194
Maps	O1	High Beta	Eyes Open	0.145	0.314	0.107	0.403	0.458	0.524
Maps	O1	Gamma	Eyes Open	0.126	0.383	0.100	0.662	0.492	0.693
Maps	O2	Delta	Eyes Open	0.123	0.395	0.017	0.686	0.908	0.998
Maps	O2	Theta	Eyes Open	0.137	0.342	0.057	0.540	0.697	0.842
Maps	O2	Alpha	Eyes Open	0.238	0.096	0.209	0.184	0.146	0.352
Maps	O2	Low Beta	Eyes Open	0.210	0.144	0.152	0.244	0.293	0.391
Maps	O2	Upper Beta	Eyes Open	0.344	0.015	0.369	0.034	0.008	0.029
Maps	O2	High Beta	Eyes Open	0.239	0.094	0.221	0.220	0.123	0.288
Maps	O2	Gamma	Eyes Open	0.176	0.222	0.162	0.518	0.261	0.608
Maps	P7	Delta	Eyes Open	0.128	0.374	0.000	0.686	0.998	0.998
Maps	P7	Theta	Eyes Open	-0.002	0.988	-0.101	0.988	0.487	0.842
Maps	P7	Alpha	Eyes Open	0.068	0.640	0.066	0.689	0.647	0.693
Maps	P7	Low Beta	Eyes Open	0.041	0.778	-0.051	0.838	0.727	0.727
Maps	P7	Upper Beta	Eyes Open	0.112	0.440	0.011	0.474	0.941	0.941
Maps	P7	High Beta	Eyes Open	0.074	0.608	-0.137	0.608	0.343	0.438
Maps	P7	Gamma	Eyes Open	0.062	0.669	-0.113	0.720	0.434	0.693
Maps	P8	Delta	Eyes Open	0.117	0.419	-0.068	0.686	0.639	0.998
Maps	P8	Theta	Eyes Open	0.275	0.053	0.090	0.441	0.534	0.842
Maps	P8	Alpha	Eyes Open	0.316	0.025	0.236	0.184	0.098	0.352
Maps	P8	Low Beta	Eyes Open	0.320	0.024	0.265	0.206	0.063	0.246
Maps	P8	Upper Beta	Eyes Open	0.436	0.002	0.371	0.013	0.008	0.029
Maps	P8	High Beta	Eyes Open	0.311	0.028	0.232	0.140	0.105	0.288
Maps	P8	Gamma	Eyes Open	0.237	0.098	0.175	0.447	0.224	0.608
Maps	T7	Delta	Eyes Open	-0.100	0.490	-0.246	0.686	0.086	0.599
Maps	T7	Theta	Eyes Open	-0.191	0.184	-0.354	0.515	0.012	0.164
Maps	T7	Alpha	Eyes Open	-0.033	0.818	-0.074	0.818	0.610	0.693
Maps	T7	Low Beta	Eyes Open	-0.092	0.524	-0.232	0.611	0.105	0.246
Maps	T7	Upper Beta	Eyes Open	-0.072	0.621	-0.262	0.621	0.067	0.093
Maps	T7	High Beta	Eyes Open	-0.137	0.344	-0.371	0.403	0.008	0.053
Maps	T7	Gamma	Eyes Open	-0.105	0.468	-0.320	0.662	0.024	0.203
Maps	T8	Delta	Eyes Open	0.198	0.168	-0.010	0.686	0.946	0.998
Maps	T8	Theta	Eyes Open	0.219	0.126	0.147	0.441	0.307	0.842
Maps	T8	Alpha	Eyes Open	0.270	0.058	0.298	0.184	0.036	0.352
Maps	T8	Low Beta	Eyes Open	0.203	0.157	0.209	0.244	0.146	0.255
Maps	T8	Upper Beta	Eyes Open	0.234	0.102	0.239	0.153	0.094	0.110
Maps	T8	High Beta	Eyes Open	0.081	0.576	0.101	0.608	0.486	0.524
Maps	T8	Gamma	Eyes Open	0.083	0.567	0.074	0.662	0.608	0.724
Vocabulary	AF3	Delta	Eyes Open	0.157	0.277	0.060	0.894	0.680	0.809
Vocabulary	AF3	Theta	Eyes Open	0.042	0.770	0.126	0.953	0.385	0.995
Vocabulary	AF3	Alpha	Eyes Open	0.176	0.220	0.044	0.343	0.760	0.819
Vocabulary	AF3	Low Beta	Eyes Open	0.194	0.177	0.183	0.394	0.202	0.883
Vocabulary	AF3	Upper Beta	Eyes Open	0.218	0.128	0.192	0.358	0.182	0.851
Vocabulary	AF3	High Beta	Eyes Open	0.130	0.370	0.196	0.991	0.173	0.806
Vocabulary	AF3	Gamma	Eyes Open	0.080	0.583	0.134	0.720	0.352	0.603
Vocabulary	AF4	Delta	Eyes Open	0.090	0.536	-0.021	0.894	0.882	0.882
Vocabulary	AF4	Theta	Eyes Open	0.062	0.666	0.170	0.953	0.238	0.995
Vocabulary	AF4	Alpha	Eyes Open	0.196	0.173	0.047	0.343	0.744	0.819
Vocabulary	AF4	Low Beta	Eyes Open	0.267	0.061	0.219	0.282	0.126	0.883
Vocabulary	AF4	Upper Beta	Eyes Open	0.253	0.077	0.201	0.269	0.161	0.851
Vocabulary	AF4	High Beta	Eyes Open	0.139	0.334	0.147	0.991	0.308	0.862
Vocabulary	AF4	Gamma	Eyes Open	0.079	0.586	0.034	0.720	0.813	0.813
Vocabulary	F3	Delta	Eyes Open	-0.136	0.346	-0.218	0.894	0.128	0.547
Vocabulary	F3	Theta	Eyes Open	0.042	0.772	0.050	0.953	0.730	0.995
Vocabulary	F3	Alpha	Eyes Open	0.165	0.253	0.011	0.354	0.941	0.941
Vocabulary	F3	Low Beta	Eyes Open	0.087	0.549	-0.029	0.641	0.843	0.963
Vocabulary	F3	Upper Beta	Eyes Open	0.182	0.206	0.039	0.380	0.790	0.942
Vocabulary	F3	High Beta	Eyes Open	-0.002	0.991	-0.129	0.991	0.372	0.868
Vocabulary	F3	Gamma	Eyes Open	-0.096	0.509	-0.153	0.720	0.288	0.603
Vocabulary	F4	Delta	Eyes Open	-0.122	0.398	-0.219	0.894	0.127	0.547
Vocabulary	F4	Theta	Eyes Open	0.043	0.768	0.119	0.953	0.411	0.995
Vocabulary	F4	Alpha	Eyes Open	0.177	0.218	0.058	0.343	0.690	0.819
Vocabulary	F4	Low Beta	Eyes Open	0.176	0.222	-0.017	0.394	0.907	0.963
Vocabulary	F4	Upper Beta	Eyes Open	0.201	0.163	0.025	0.379	0.865	0.942
Vocabulary	F4	High Beta	Eyes Open	0.010	0.946	0.030	0.991	0.834	0.919
Vocabulary	F4	Gamma	Eyes Open	-0.114	0.430	-0.173	0.720	0.230	0.603
Vocabulary	F7	Delta	Eyes Open	0.156	0.279	0.057	0.894	0.693	0.809
Vocabulary	F7	Theta	Eyes Open	0.087	0.550	0.201	0.953	0.161	0.995
Vocabulary	F7	Alpha	Eyes Open	0.188	0.192	0.081	0.343	0.574	0.819
Vocabulary	F7	Low Beta	Eyes Open	0.215	0.133	0.111	0.394	0.444	0.888
Vocabulary	F7	Upper Beta	Eyes Open	0.258	0.070	0.111	0.269	0.443	0.942
Vocabulary	F7	High Beta	Eyes Open	0.142	0.325	0.006	0.991	0.968	0.968
Vocabulary	F7	Gamma	Eyes Open	0.072	0.617	-0.101	0.720	0.487	0.603
Vocabulary	F8	Delta	Eyes Open	-0.050	0.729	-0.029	0.894	0.840	0.882
Vocabulary	F8	Theta	Eyes Open	0.001	0.995	0.042	0.995	0.773	0.995
Vocabulary	F8	Alpha	Eyes Open	0.208	0.148	0.102	0.343	0.481	0.819
Vocabulary	F8	Low Beta	Eyes Open	0.175	0.225	0.090	0.394	0.536	0.899
Vocabulary	F8	Upper Beta	Eyes Open	0.178	0.217	0.028	0.380	0.846	0.942
Vocabulary	F8	High Beta	Eyes Open	0.013	0.928	0.085	0.991	0.555	0.919
Vocabulary	F8	Gamma	Eyes Open	-0.104	0.473	-0.096	0.720	0.506	0.603
Vocabulary	FC5	Delta	Eyes Open	-0.073	0.617	-0.204	0.894	0.156	0.547
Vocabulary	FC5	Theta	Eyes Open	-0.172	0.233	-0.092	0.953	0.525	0.995
Vocabulary	FC5	Alpha	Eyes Open	0.063	0.664	-0.126	0.721	0.384	0.819
Vocabulary	FC5	Low Beta	Eyes Open	0.049	0.737	0.021	0.737	0.885	0.963
Vocabulary	FC5	Upper Beta	Eyes Open	0.085	0.559	0.023	0.711	0.875	0.942
Vocabulary	FC5	High Beta	Eyes Open	0.006	0.970	-0.027	0.991	0.853	0.919
Vocabulary	FC5	Gamma	Eyes Open	-0.019	0.897	-0.094	0.897	0.517	0.603
Vocabulary	FC6	Delta	Eyes Open	-0.166	0.248	-0.224	0.894	0.118	0.547
Vocabulary	FC6	Theta	Eyes Open	-0.037	0.799	-0.077	0.953	0.596	0.995
Vocabulary	FC6	Alpha	Eyes Open	0.110	0.446	0.061	0.568	0.672	0.819
Vocabulary	FC6	Low Beta	Eyes Open	0.109	0.450	0.159	0.610	0.269	0.883
Vocabulary	FC6	Upper Beta	Eyes Open	0.054	0.710	0.062	0.764	0.669	0.942
Vocabulary	FC6	High Beta	Eyes Open	-0.018	0.902	0.028	0.991	0.845	0.919
Vocabulary	FC6	Gamma	Eyes Open	-0.101	0.485	-0.123	0.720	0.395	0.603
Vocabulary	O1	Delta	Eyes Open	-0.096	0.506	-0.117	0.894	0.419	0.653
Vocabulary	O1	Theta	Eyes Open	-0.049	0.734	-0.002	0.953	0.987	0.995
Vocabulary	O1	Alpha	Eyes Open	0.185	0.198	0.044	0.343	0.760	0.819
Vocabulary	O1	Low Beta	Eyes Open	0.102	0.480	-0.081	0.610	0.578	0.899
Vocabulary	O1	Upper Beta	Eyes Open	0.163	0.258	-0.043	0.401	0.767	0.942
Vocabulary	O1	High Beta	Eyes Open	-0.033	0.818	-0.164	0.991	0.254	0.862
Vocabulary	O1	Gamma	Eyes Open	-0.108	0.457	-0.179	0.720	0.214	0.603
Vocabulary	O2	Delta	Eyes Open	-0.043	0.766	-0.124	0.894	0.390	0.653
Vocabulary	O2	Theta	Eyes Open	0.026	0.859	0.045	0.953	0.755	0.995
Vocabulary	O2	Alpha	Eyes Open	0.242	0.090	0.109	0.343	0.451	0.819
Vocabulary	O2	Low Beta	Eyes Open	0.280	0.049	0.008	0.282	0.954	0.963
Vocabulary	O2	Upper Beta	Eyes Open	0.293	0.039	0.086	0.269	0.553	0.942
Vocabulary	O2	High Beta	Eyes Open	0.058	0.692	-0.079	0.991	0.588	0.919
Vocabulary	O2	Gamma	Eyes Open	-0.081	0.574	-0.142	0.720	0.327	0.603
Vocabulary	P7	Delta	Eyes Open	-0.123	0.395	-0.105	0.894	0.466	0.653
Vocabulary	P7	Theta	Eyes Open	-0.171	0.234	0.001	0.953	0.992	0.995
Vocabulary	P7	Alpha	Eyes Open	0.062	0.670	0.047	0.721	0.745	0.819
Vocabulary	P7	Low Beta	Eyes Open	-0.054	0.712	-0.147	0.737	0.307	0.883
Vocabulary	P7	Upper Beta	Eyes Open	0.001	0.994	-0.159	0.994	0.269	0.941
Vocabulary	P7	High Beta	Eyes Open	-0.127	0.378	-0.269	0.991	0.059	0.806
Vocabulary	P7	Gamma	Eyes Open	-0.155	0.281	-0.281	0.720	0.048	0.603
Vocabulary	P8	Delta	Eyes Open	-0.067	0.646	-0.172	0.894	0.232	0.651
Vocabulary	P8	Theta	Eyes Open	0.100	0.491	-0.001	0.953	0.995	0.995
Vocabulary	P8	Alpha	Eyes Open	0.241	0.092	0.130	0.343	0.369	0.819
Vocabulary	P8	Low Beta	Eyes Open	0.296	0.037	0.127	0.282	0.379	0.883
Vocabulary	P8	Upper Beta	Eyes Open	0.313	0.027	0.130	0.269	0.368	0.942
Vocabulary	P8	High Beta	Eyes Open	0.082	0.574	-0.047	0.991	0.748	0.919
Vocabulary	P8	Gamma	Eyes Open	-0.049	0.736	-0.180	0.793	0.212	0.603
Vocabulary	T7	Delta	Eyes Open	0.006	0.968	-0.147	0.968	0.308	0.653
Vocabulary	T7	Theta	Eyes Open	-0.202	0.160	-0.186	0.953	0.196	0.995
Vocabulary	T7	Alpha	Eyes Open	0.012	0.935	-0.113	0.935	0.435	0.819
Vocabulary	T7	Low Beta	Eyes Open	-0.109	0.450	-0.134	0.610	0.352	0.883
Vocabulary	T7	Upper Beta	Eyes Open	-0.097	0.502	-0.222	0.703	0.121	0.851
Vocabulary	T7	High Beta	Eyes Open	-0.132	0.359	-0.219	0.991	0.126	0.806
Vocabulary	T7	Gamma	Eyes Open	-0.108	0.454	-0.222	0.720	0.121	0.603
Vocabulary	T8	Delta	Eyes Open	0.006	0.968	-0.113	0.968	0.436	0.653
Vocabulary	T8	Theta	Eyes Open	0.021	0.885	-0.037	0.953	0.800	0.995
Vocabulary	T8	Alpha	Eyes Open	0.179	0.213	0.109	0.343	0.451	0.819
Vocabulary	T8	Low Beta	Eyes Open	0.186	0.196	0.007	0.394	0.963	0.963
Vocabulary	T8	Upper Beta	Eyes Open	0.057	0.693	-0.001	0.764	0.994	0.994
Vocabulary	T8	High Beta	Eyes Open	-0.098	0.497	-0.068	0.991	0.639	0.919
Vocabulary	T8	Gamma	Eyes Open	-0.103	0.477	-0.072	0.720	0.619	0.667

Topological Maps

Rdata_eo <- Rdata_eo %>% 
  mutate(electrode = Channel)
Rdata_eo <- electrode_locations(Rdata_eo)

Now we can visualize the results

if (!"Significance" %in% names(Rdata_eo)) {
  Rdata_eo <- Rdata_eo %>%
    add_column(Significance = "p > .05")
}
Rdata_eo$Significance[Rdata_eo$p < 0.05] <- "p < .05"
Rdata_eo$Significance[Rdata_eo$q < 0.05] <- "q < .05"
Rdata_eo$Significance <- factor(Rdata_eo$Significance, 
                                levels=c("p > .05", "p < .05", "q < .05"))

Rdata_eo_sub <- Rdata_eo %>% 
  filter(Band %in% c("Theta", "Alpha", "Low Beta", "Upper Beta", "High Beta"))

ggplot(Rdata_eo_sub, aes(x = x,
                     y = y,
                     fill = r,
                     label = Channel)) +
  scale_fill_distiller(palette = "RdBu",
                       limits=c(-0.45, 0.45)) +
  theme_void() +
  coord_equal() +
  facet_grid(material~Band, switch="both") +
  #labs(fill = expression(paste("Correlation with ", alpha))) +
  labs(fill = "Correlation") +
  stat_scalpmap(grid_res = 200,
                interp_limit = "skirt",  # head or skirt
                method = "biharmonic")+
  #geom_mask(scale_fac = 1.6) +
  geom_head(color="black") +
  geom_channels(geom = "text",
                size = 3,
                vjust = 1.5
  ) +
  geom_channels(geom = "point",
                size = 2,
                aes(col = Significance)) +
  scale_color_manual(values = c("black", 
                                "purple", 
                                "gold")) +
  ggtitle(expression(paste("Correlations Between Spectral Power and ", 
                           alpha, 
                           ", Eyes Open"))) +
  theme(plot.title = element_text(hjust = 0.5))

ggsave("images/topo_eo.pdf", width=9, height = 3.5)
ggsave("images/figure6.png", width=9, height = 3.5, dpi=300)

Distributions of r and p values

And here is the corresponding distributions of r and p values. The dashed lines correspond to a significant threshold of p < .05 on either a r or a p-value scale.

ggplot(Rdata_eo, aes(x = Band, y = r, col = Channel)) +
  geom_point() +
  stat_summary(fun.data = "mean_se", col="black", 
               alpha=0.5, geom = "errorbar") +
  facet_wrap(~ material) +
  ggtitle("Correlation, by Band") +
  ylab("r value") +
  annotate("segment", x=-Inf, xend=Inf, y=0.28, yend = 0.28, lty=2) +
  annotate("segment", x=-Inf, xend=Inf, y=-0.28, yend = -0.28, lty=2) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  theme_pander()

ggplot(Rdata_eo, aes(x = Band, y = p, col = Channel)) +
  geom_point() +
  stat_summary(fun.data = "mean_se", col="black", 
               alpha=0.5, geom = "errorbar") +
  ggtitle("p-value, by Band") +
  ylab("p value") +
  facet_wrap(~ material) +
  annotate("segment", x=-Inf, xend=Inf, y=0.05, yend = 0.05, lty=2) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  scale_y_log10() +
  theme_pander()

Now, we might wonder how much the correlations change from eyes closed to eyes open recordings. The best way to chack is to visualize them as a scatterplot:

Rdata <- bind_rows(Rdata_ec, Rdata_eo)
wrdata <- pivot_wider(Rdata, id_cols = c("material", "Channel", "Band"),
                      names_from = Recording, 
                      values_from = r)

ggplot(wrdata, aes(x = `Eyes Open`, y = `Eyes Closed`, 
                   col = material)) +
  annotate("segment", x = 0, y=-Inf, xend=0, 
           yend=Inf, col="grey", lwd=1, lty=1) +
  annotate("segment", x = -Inf, y=0, xend=Inf, 
           yend=0, col="grey", lwd=1, lty=1) +
  
  geom_point(alpha=0.5, size=3) +
  annotate("segment", x = -0.25, y=-0.25, xend=0.5, 
           yend=0.5, col="black", lwd=1, lty=2) +
  theme(plot.title = element_text(hjust = 0.5)) +
  #geom_smooth(method = "lm", formula = y ~ x, 
  #            fullrange = T, lwd = 1) +
  scale_color_brewer(palette="Dark2") +
  ggtitle("Correlation Coefficients Across Recordings") +
  theme_pander()

Let’s tabulate only those channels that survive the FDR correction:

survivors <- Rdata_eo %>%
  filter(q < 0.05 | rob_q < 0.05)
  
survivors %>%
  xtable() %>%
  kable(digits = 3) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

material	Channel	Band	Recording	r	p	rob_r	q	rob_p	rob_q	electrode	radius	theta	phi	cart_x	cart_y	cart_z	x	y	Significance
Maps	AF3	Upper Beta	Eyes Open	0.383	0.006	0.389	0.021	0.005	0.029	AF3	1	-76	-66	-33.70	76.84	21.23	-30.91	69.43	q < .05
Maps	AF4	Upper Beta	Eyes Open	0.383	0.006	0.403	0.021	0.004	0.029	AF4	1	76	65	35.71	77.73	21.96	32.12	68.88	q < .05
Maps	AF4	High Beta	Eyes Open	0.315	0.026	0.417	0.140	0.003	0.036	AF4	1	76	65	35.71	77.73	21.96	32.12	68.88	p < .05
Maps	F3	Upper Beta	Eyes Open	0.326	0.021	0.292	0.037	0.040	0.070	F3	1	-60	-47	-50.24	53.11	42.19	-40.92	43.88	q < .05
Maps	F4	Upper Beta	Eyes Open	0.338	0.016	0.338	0.034	0.016	0.038	F4	1	61	46	51.84	54.30	40.81	42.37	43.88	q < .05
Maps	F7	Upper Beta	Eyes Open	0.428	0.002	0.357	0.013	0.011	0.030	F7	1	-98	-31	-70.26	42.47	-11.42	-84.00	50.47	q < .05
Maps	F8	Upper Beta	Eyes Open	0.336	0.017	0.317	0.034	0.025	0.050	F8	1	98	31	73.04	44.42	-12.00	84.00	50.47	q < .05
Maps	O2	Upper Beta	Eyes Open	0.344	0.015	0.369	0.034	0.008	0.029	O2	1	86	-75	29.84	-112.16	8.80	22.26	-83.07	q < .05
Maps	P8	Upper Beta	Eyes Open	0.436	0.002	0.371	0.013	0.008	0.029	P8	1	91	-45	73.06	-73.07	-2.54	64.35	-64.35	q < .05

In the case of eyes-open data, we have a much larger set of channels that survive correction for multiple comparison. Although these correlations generally reflect the same trends we have seen the eyes-closed data (i.e., significant positive correlations for prefrontal and right parietal sites), they are now stroger for visual (not verbal) materials and centered at at slightly higher frequency (upper beta instead of low beta).

Here is an overview of the scatterplots and correlations:

focus <- Fdata_eo %>%
  filter(material == "Maps", 
         Channel %in% survivors$Channel,
         Band == "Upper Beta") %>%
  rename(Alpha = alpha)

ggplot(focus, aes(x=Alpha, y=Power)) +
  geom_point(size=4, alpha=0.5, col="black") + 
  geom_smooth(method = "lm", formula = y ~ x, 
              col="red", fill="red", fullrange = T, lwd=2) +
  theme_pander() +
  scale_x_continuous() +   
  scale_y_continuous() + 
  ggtitle("Rate of Forgetting and Eyes-Open Beta Power") +
  xlab(expression(paste(alpha, " Maps"))) +
  ylab("Upper Beta Power (15-18 Hz)") +
  facet_wrap(~ Channel, scales="free_y") +
  geom_text(data=survivors, col="red",
            mapping=aes(x=0.25, y=9.5, 
                        label= paste("r =", round(r, 2)))) +
  theme(panel.spacing = unit(1.5, "lines")) +
  theme(plot.title = element_text(hjust = 0.5))

ggsave("images/figure7.png")

## Saving 9 x 9 in image

Summary

In general, posterior (P8) and frontal channels (AF3/4, F7/8) are the most commonly associated with rate of forgetting. The correlations mostly happen in the beta range.

Rdata_g <- full_join(Rdata_ec, Rdata_eo)

## Joining, by = c("material", "Channel", "Band", "Recording", "r", "rob_r", "p", "q", "rob_p", "rob_q", "electrode", "radius", "theta", "phi", "cart_x", "cart_y", "cart_z", "x", "y", "Significance")

Rdata_g$Band <- recode(Rdata_g$Band, 
                       `Low Beta` = "Low\nBeta", 
                       `Upper Beta` = "Upper\nBeta",
                       `High Beta` = "High\nBeta")

Rdata_g <- Rdata_g %>%
  mutate(Material = material)

ggplot(filter(Rdata_g, 
              p < 0.05),
#              !Band %in% c("Delta", "Gamma")),
       aes(x=Band, fill=Channel, alpha=Material)) + 
  geom_bar(col="white") +
  stat_count(geom = "text", colour = "black", size = 2.5,
aes(label = Channel),position=position_stack(vjust=0.5))+
  scale_alpha_discrete(range=c(0.4, 0.9)) +
  #geom_text(aes(stat="identity", label=Channel,
  #              position=position_stack())) +
  xlab("Frequency Band") +
  ylab("Number of Significant Instances") +
  ggtitle("Significant Channels Across All Data") +
  theme_pander() + 
  theme(plot.title = element_text(hjust = 0.5)) +
  scale_fill_d3()

## Warning: Using alpha for a discrete variable is not advised.

ggsave(filename = "images/figure8.png", dpi = 600)

## Saving 6 x 5 in image

Similarity of Findings Across Materials

So far, the results seem pretty similar across materials (Maps vs. Vocabulary). But how similar are the topomaps? To do so, we can compute the correlation between the vector of r values for every channel and frequency band that were computed for Maps and Vocabulary. Two separate correlations will be run, one for Eyes Closed Recordings and one for Eyes Open recordings:

Rdata_g$Recording <- recode(Rdata_g$Recording, 
                            `Eyes Open` = "EyesOpen", 
                            `Eyes Closed` = "EyesClosed")

Rdata_g %>% 
  pivot_wider(id_cols = c("Channel", "Band", "Recording"), 
              names_from = c("material"),
              values_from=c("r")) %>%
  group_by(Recording) %>%
  filter(! Band %in% c("Delta", "Gamma")) %>%
  summarize(r=cor(Maps, Vocabulary),
            p=cor.test(Maps, Vocabulary)$p.value,
            df=cor.test(Maps, Vocabulary)$parameter) %>%
  xtable() %>%
  kable(digits = 6) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

Recording	r	p	df
EyesClosed	0.605679	0	68
EyesOpen	0.814669	0	68

Similarity of Findings Across Recordings

and these are the correlations within materials, across recording modality:

Rdata_g %>% 
  pivot_wider(id_cols = c("Channel", "Band", "material"), 
              names_from = c("Recording"),
              values_from=c("r")) %>%
  group_by(material) %>%
  filter(! Band %in% c("Delta", "Gamma")) %>%
  summarize(r=cor(EyesOpen, EyesClosed),
            p=cor.test(EyesOpen, EyesClosed)$p.value,
            df=cor.test(EyesOpen, EyesClosed)$parameter) %>%
  xtable() %>%
  kable(digits = 6) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

material	r	p	df
Maps	0.860708	0	68
Vocabulary	0.818843	0	68